Kaggle Competition -- Finding Donors for a Charity with an AUC of 0.94

Comparing Random Forest, Gradient Boosting, and XGBoost to select the best model to predict potential donors for a Charity. This project will employ 3 supervised algorithms, including Random Forest, Gradient Boosting, and XGBoost, to accurately model individuals' income using the 1994 U.S. Census data. I will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. My goal with this implementation is to construct a model that accurately predicts whether an individual makes more than 50,000 dollars. This sort of task can arise in a non-profit setting, where organizations survive on donations.